Identification and Estimation of Industry Dynamic Models with Persistent and Hidden State Variables

本研究的重点是生产者进入、成长和退出的结构模型的构建和估计。数据源是新的；一份酒精税申报单，包括出生日期、离开日期以及德克萨斯州所有有执照的餐馆和酒吧每月酒精销售的完整历史记录。在该模型中，一家公司的销售额与其利润成正比，但短暂的成本冲击使其成为衡量盈利能力持久成分的不完美指标。由于这种不完美的观察，模型的状态变量既持久又隐藏。状态变量的持久性将此模型与许多包含Rust（1987）条件独立性假设的动态离散选择的可估计模型区分开来。为了区分盈利能力的持续性和暂时性成分，本项目使用了生产商退出决策仅依赖于持续性成分的事实。在具有正态分布冲击的模型中，生产者退出决策中的信息确定了描述盈利能力的持久和短暂成分以及生产者的最佳退出阈值的参数。本研究项目的一个重要组成部分是将该识别证明扩展到半参数和非参数环境。该项目最初的实证研究侧重于区分创业学习的高斯模型（类似于Jovanovic的（1982））和具有完美创业信息的模型（如Hopenhayn的（1992））。数据集中描述每个餐厅或酒吧的位置及其母公司的特征的附加信息表明该模型和估计技术的进一步推广。

This research project focuses on the formulation and estimation of structural models of producer entry, growth, and exit. The data source is new; a file of alcohol tax returns that contains the date of birth, date of exit, and a complete monthly history of the dollar value of alcohol sales for all licensed restaurants and bars in Texas. In the model, a firm's sales is proportional to its profits, but transitory cost shocks make it an imperfect indicator of profitability's persistent component. Because of this imperfect observation, the model's state variable is both persistent and hidden. The state variable's persistence distinguishes this model from the many estimable models of dynamic discrete choice that incorporate Rust's (1987) conditional independence assumption. To disentangle the persistent and transitory components of profitability, this project uses the fact that producers' exit decisions depend only on the persistent component. In the model with normally distributed shocks, the information in producers' exit decisions identifies the parameters describing both the persistent and transitory components of profitability as well as the producer's optimal exit threshold. An important component of this research project is the extension of this identification proof to semi-parametric and non parametric environments. The project's initial empirical research focuses on distinguishing Gaussian models of entrepreneurial learning similar to Jovanovic's (1982) from models with perfect entrepreneurial information, such as Hopenhayn's (1992). Additional information in the data set describing each restaurant or bar's location and the characteristics of its parent firm suggest further generalizations of the model and estimation technique.